It is expected future wireless communication networks based on Orthogonal Frequency Division Multiple Access (OFDMA) will be capable of serving a greater number of users with fulfilled Quality of Service (QoS) and having higher system spectral efficiency compared to past and present systems. Downlink scheduling and Radio Resource Allocation (RRA) algorithms have a key role in succeeding in this task, due to their capacity to adapt operation to the time variant wireless channels.
The research in scheduling and RRA algorithms in OFDMA networks has in general focused in assuring some kind of fairness among users while increasing system spectral efficiency. Examples may be found in e.g. “Downlink Scheduling and Radio Resource Allocation in Adaptive OFDMA Wireless Communication Systems for User-Individual QoS,” Transactions on Engineering, Computing and Technology—World Enformatika Society, March 2006, by Lu Yanhui, Wang Chunming, Yin Changchuan and Tue Guangxin [1], or in “A Proportional Fairness Algorithm with QoS Provision in Downlink OFDMA Systems,” IEEE Communications Letters, v.10, n.11, November 2006, by Tien-Dzung Nguyen and Youngnam Han [2], or in “A Low Complexity Algorithm for Proportional Resource Allocation in OFDMA Systems,” IEEE Workshop on Signal Processing Systems, pp.1-6, October 2004, by I. C. Wong, Zukang Shen, B. L. Evans and J. G. Andrews [3]. One shortcoming of the state-of-art scheduling and RRA algorithms is that they do not address whether the users of the system becomes satisfied or not.
One approach, the Rate Maximization (RM), represents the upper bound in the system spectral efficiency, e.g. as shown by J. Gross and M. Bohge, “Dynamic Mechanisms in OFDM Wireless Systems: A Survey on Mathematical and System Engineering Contributions,” Telecommunication Networks Group (TKN) Technical Report TKN-06-001, Technical University Berlin, Germany, May 2006 [4]. This algorithm assigns a given subcarrier to the user that experiences the highest channel gain on it. Regarding the RM scheduler, although it achieves a high system spectral efficiency, it is generally known that this scheduling algorithm provides starvation of terminals being present in the vicinity of a cell edge.
Margin Adaptive (MA) and Rate Adaptive (RA) approaches are RRA problems designed to achieve different objectives [4]. The former has the objective of minimizing the total used power while the user data rate requirements of each user at each Time Transmission Interval (TTI) have to be fulfilled. The latter problem aims at maximizing the minimum user allocated data rate at each TTI. The MA and RA algorithms aim at solving RRA problems in OFDMA systems based only on the current system conditions. They do not take into account e.g. the effect of past allocations when allocating resources to the users at the current TTI.
Weighted Multi-Carrier Proportional Fair (WMPF) scheduling algorithm is a generalization of Weighted Proportional Fair (WPF) algorithm to the multi-carrier case. WMPF scheduler is a modification of Multi-Carrier Proportional Fair (MPF) scheduler to deal with different rate requirements, and is e.g. described by Hoon Kim, Keunyoung Kim, Youngnam Han and Sangboh Yun, “A Proportional Fair Scheduling for Multicarrier Transmission Schemes,” IEEE Communications Letters, v.9, n.3, pp. 210-212, 2005 [5]. WMPF consists of a scheduling algorithm that gives opportunity of transmission in a given subcarrier to the terminal that has the greatest priority. The WMPF priority function of a terminal in a given subcarrier takes into account the current channel condition, the user average data rate requirement and the average data rate perceived by the user due to the past allocations. The WMPF scheduling algorithm does thereby take into account the effect of past allocation in the current scheduling decision. This is done by the average data rate perceived by the user due to the past allocations in the denominator of WMPF priority function. However, this algorithm may in certain situation operate to satisfy a few users while sacrificing a large number of other users.
A general problem with prior-art solutions is that no or little concern is taken to the actual total user satisfaction.